"""simple docstring"""



import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def 	_lowerCamelCase(	a=3_2   ,    a=1_0   ,    a=1_0_0   ,    a=1_0_2_6   ,    a=True   ,    a="data/tokenized_stories_train_wikitext103.jbl"   ,    a="igf_context_pairs.jbl"   ,    ):
       set_seed(3				)
       # generate train_data and objective_set
       __a ,   __a       =    generate_datasets(
           a   ,    a   ,    number=a   ,    min_len=1_0_2_6   ,    trim=a				)
       # keeps model same across runs
       set_seed(4				)
       # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
       # can we train on GPU?
       __a       =    torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)

       # load pretrained model
       __a       =    load_gpta("gpt2"				).to(a				)
       print("computing perplexity on objective set"				)
       __a       =    compute_perplexity(a   ,    a   ,    a				).item()
       print("perplexity on objective set:"   ,    a				)

       # collect igf pairs and save to file demo.jbl
       collect_objective_set(a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				)

       # clean up, delete model and data we don't need anymore
       del model, train_data, objective_set
       torch.cuda.empty_cache()



def 	_lowerCamelCase(	a   ,    a=1_5   ,    a=1_2_8   ,    a=1_0_0   ,    a="igf_model.pt"   ,    ):
       set_seed(4_2				)

       # Load pre-trained model
       __a       =    GPTaLMHeadModel.from_pretrained("gpt2"				)

       # Initialize secondary learner to use embedding weights of model
       __a       =    SecondaryLearner(a				)

       # Train secondary learner
       __a       =    train_secondary_learner(
           a   ,    a   ,    max_epochs=a   ,    batch_size=a   ,    eval_freq=1_0_0   ,    igf_model_path=a   ,    )

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a=3_2   ,    a=1_0_0_0   ,    a=1_6   ,    a=1.0   ,    a=recopy_gpta   ,    a=None   ,    a=1_0   ,    a="gpt2_finetuned.pt"   ,    ):
       __a       =    torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)
       __a       =    RandomSampler(a				)
       __a       =    DataLoader(a   ,    sampler=a				)

       __a       =    max_steps // (len(a				)) + 1
       __a       =    0
       __a       =    torch.zeros((1, context_len)   ,    dtype=torch.long   ,    device=a				)
       __a ,   __a ,   __a       =    recopy_model(a   ,    a   ,    a				)

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(a				)
              secondary_learner.eval()
       __a       =    []
       __a       =    0

       __a       =    []
       __a       =    []

       # Compute the performance of the transformer model at the beginning
       __a       =    compute_perplexity(a   ,    a   ,    a				)
       test_perps.append(a				)
       print("Test perplexity, step"   ,    a   ,    ":"   ,    a				)
       for epoch in range(int(a				)				):
              for step, example in enumerate(a				):
                     torch.cuda.empty_cache()
                     __a       =    random.randint(0   ,    example.size(2				) - context_len - 1				)
                     __a       =    example[0, 0, start : start + context_len]
                     lm_optimizer.zero_grad()
                     __a       =    model(a   ,    labels=a				)
                     __a       =    True

                     if secondary_learner is not None:
                            __a       =    secondary_learner.forward(
                                torch.tensor(a   ,    dtype=torch.long   ,    device=a				).unsqueeze(0				)				)[0].item()
                            observed_qs.append(float(a				)				)

                            # Here we implement the simple non-constant threshold for the predicted IG(X) value
                            # We will decay the selectivity of our secondary learner filter from
                            # 1 standard deviation above average to 1 below average after 10 batches.

                            if global_step == 1_0:
                                   __a       =    -1
                            if predicted_q < threshold:
                                   __a       =    False

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()				)				)
                            __a       =    outputs[0]
                            lm_loss.backward()
                            examples += 1

                     del outputs

                     # Once the batch is filled with enough contexts, backprop on the batch.
                     if examples == batch_size:
                            torch.cuda.empty_cache()
                            __a       =    0
                            # Do LM backprop
                            torch.nn.utils.clip_grad_norm_(model.parameters()   ,    3.0				)
                            lm_optimizer.step()
                            lm_scheduler.step()  # Update learning rate schedule
                            global_step += 1
                            # Compute the performance of the transformer model at this batch
                            if global_step % eval_interval == 0:
                                   __a       =    compute_perplexity(a   ,    a   ,    a				)
                                   test_perps.append(a				)

                                   print("Test perplexity, step"   ,    a   ,    ":"   ,    a				)
            # Break out of the loop after 60 batches
                     if max_steps > 0 and global_step > 6_0:
                            break
              if max_steps > 0 and global_step > 6_0:
                     break

    # save finetuned transformer model
       torch.save(model.state_dict()   ,    a				)
       torch.cuda.empty_cache()
       # Do some cleaning up so we can reinitialize for the next run of this function
       del lm_optimizer
       del lm_scheduler
       return model



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"				)

       # Required parameters
       parser.add_argument(
           "--data_dir"   ,    default=a   ,    type=a   ,    required=a   ,    help="The input data dir. Should contain data files for WikiText."   ,    )
       parser.add_argument(
           "--model_name_or_path"   ,    default=a   ,    type=a   ,    required=a   ,    help="Path to pretrained model or model identifier from huggingface.co/models"   ,    )
       parser.add_argument(
           "--data_file"   ,    type=a   ,    default=a   ,    help=(
               "A jbl file containing tokenized data which can be split as objective dataset, "
               "train_dataset and test_dataset."
           )   ,    )

       parser.add_argument(
           "--igf_data_file"   ,    type=a   ,    default=a   ,    help="A jbl file containing the context and information gain pairs to train secondary learner."   ,    )

       parser.add_argument(
           "--output_dir"   ,    default=a   ,    type=a   ,    required=a   ,    help="The output directory where the final fine-tuned model is stored."   ,    )

       parser.add_argument(
           "--tokenizer_name"   ,    default=a   ,    type=a   ,    help="Pretrained tokenizer name or path if not the same as model_name"   ,    )
       parser.add_argument("--seed"   ,    type=a   ,    default=a   ,    help="A seed for reproducible training."				)

       parser.add_argument(
           "--context_len"   ,    default=3_2   ,    type=a   ,    help=(
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )   ,    )

       parser.add_argument(
           "--size_objective_set"   ,    default=1_0_0   ,    type=a   ,    help="number of articles that are long enough to be used as our objective set"   ,    )
       parser.add_argument(
           "--eval_freq"   ,    default=1_0_0   ,    type=a   ,    help="secondary model evaluation is triggered at eval_freq"				)

       parser.add_argument("--max_steps"   ,    default=1_0_0_0   ,    type=a   ,    help="To calculate training epochs"				)

       parser.add_argument(
           "--secondary_learner_batch_size"   ,    default=1_2_8   ,    type=a   ,    help="batch size of training data for secondary learner"   ,    )

       parser.add_argument(
           "--batch_size"   ,    default=1_6   ,    type=a   ,    help="batch size of training data of language model(gpt2) "				)

       parser.add_argument(
           "--eval_interval"   ,    default=1_0   ,    type=a   ,    help=(
               "decay the selectivity of our secondary learner filter from"
               "1 standard deviation above average to 1 below average after 10 batches"
           )   ,    )

       parser.add_argument(
           "--number"   ,    default=1_0_0   ,    type=a   ,    help="The number of examples split to be used as objective_set/test_data"				)

       parser.add_argument(
           "--min_len"   ,    default=1_0_2_6   ,    type=a   ,    help="The minimum length of the article to be used as objective set"				)

       parser.add_argument(
           "--secondary_learner_max_epochs"   ,    default=1_5   ,    type=a   ,    help="number of epochs to train secondary learner"				)

       parser.add_argument("--trim"   ,    default=a   ,    type=a   ,    help="truncate the example if it exceeds context length"				)

       parser.add_argument(
           "--threshold"   ,    default=1.0   ,    type=a   ,    help=(
               "The threshold value used by secondary learner to filter the train_data and allow only"
               " informative data as input to the model"
           )   ,    )

       parser.add_argument("--finetuned_model_name"   ,    default="gpt2_finetuned.pt"   ,    type=a   ,    help="finetuned_model_name"				)

       parser.add_argument(
           "--recopy_model"   ,    default=a   ,    type=a   ,    help="Reset the model to the original pretrained GPT-2 weights after each iteration"   ,    )

       # function calls
       # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
       generate_n_pairs(
           context_len=3_2   ,    max_steps=1_0   ,    size_objective_set=1_0_0   ,    min_len=1_0_2_6   ,    trim=a   ,    data_file="data/tokenized_stories_train_wikitext103.jbl"   ,    igf_data_file="igf_context_pairs.jbl"   ,    )

       # Load train data for secondary learner
       __a       =    joblib.load("data/IGF_values.jbl"				)

       # Train secondary learner
       __a       =    training_secondary_learner(
           a   ,    secondary_learner_max_epochs=1_5   ,    secondary_learner_batch_size=1_2_8   ,    eval_freq=1_0_0   ,    igf_model_path="igf_model.pt"   ,    )

       # load pretrained gpt2 model
       __a       =    GPTaLMHeadModel.from_pretrained("gpt2"				)
       set_seed(4_2				)

       # Generate train and test data to train and evaluate gpt2 model
       __a ,   __a       =    generate_datasets(
           context_len=3_2   ,    file="data/tokenized_stories_train_wikitext103.jbl"   ,    number=1_0_0   ,    min_len=1_0_2_6   ,    trim=a				)

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           a   ,    a   ,    a   ,    context_len=3_2   ,    max_steps=1_0_0_0   ,    batch_size=1_6   ,    threshold=1.0   ,    recopy_model=a   ,    secondary_learner=a   ,    eval_interval=1_0   ,    finetuned_model_name="gpt2_finetuned.pt"   ,    )


if __name__ == "__main__":
   main()

"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
